1. Field
Exemplary embodiments relate to methods and apparatuses for generating a magnetic resonance image.
2. Description of the Related Art
Magnetic resonance imaging (MRI) apparatuses are used to obtain an image of a subject by using a magnetic field, to accurately diagnose diseases since MRI apparatuses show a stereoscopic image of bones, discs, joints, nerve ligaments, etc., at a desired angle.
The MRI apparatus obtains a magnetic resonance (MR) signal and reconstructs the obtained MR signal to output an image. The MRI apparatus obtains an MR signal by using radio frequency (RF) coils, a permanent magnet, a gradient coil, and the like. When an MR signal is obtained, an erroneous signal may be generated because the MR signal is not measured in joining parts of adjacent RF coils. Therefore, noise or artifacts may be present in a reconstructed magnetic resonance image due to the invalid non-measured signal or incorrectly measured signal. In addition, while K-space data obtained from RF coils is reconstructed as a magnetic resonance image, noise in the K-space data may be amplified. Below, the invalid signal referred as ‘non-measured signal’.
Accordingly, in order to output a magnetic resonance image from which the artifacts and noise are removed, an MR signal has to be corrected by performing image processing such as calibration or the like.
MRI methods of processing an obtained MR signal include sensitivity encoding (SENSE) method, a generalized auto-calibrating partially parallel acquisition (GRAPPA) method, and the like.
An image-based imaging method, such as the SENSE method, obtains coil sensitivity information by separating an image corresponding to each individual coil through self-calibration in an image space. An image of each individual coil is obtained by performing an inverse Fourier transform on central portion of data in a K-space which has been Nyquist-sampled. In the case of reconstructing a magnetic resonance image by using the coil sensitivity information, image-based self-calibration needs very accurate coil sensitivity information.
Accordingly, in a central portion of K-space data, a large number of calibration signals is required, and a time taken to form an image increases. In addition, when a field of view (FOV) is smaller than a subject to be imaged, the image-based self-calibration may cause residual aliasing artifacts during image reconstruction.
A K-space-based imaging method, such as the GRAPPA method, calculates spatial correlations (or convolution kernels) between a calibration signal and an adjacent measured source signal through self-calibration. The GRAPPA method does not need accurate coil sensitivity information and is not limited in reconstruction of the FOV. However, when pieces of data of an image signal are damaged due to noise and spatial correlations are changed, residual aliasing artifacts and amplified noise occur in a reconstructed image.
Accordingly, there is a need for MRI methods and apparatuses which are capable of reducing aliasing artifacts and noise occurring when pieces of data of an MR image signal are missing or damaged.